Reinforcement Learning Day 2021

This event has now concluded.

Thursday, January 14, 2021

Time (EST)	Session		Speaker
10:00 AM-10:15 AM	Welcome Remarks		Akshay Krishnamurthy (opens in new tab), Microsoft Research
10:15 AM-11:00 AM	New Advances in Hierarchical Reinforcement Learning		Doina Precup (opens in new tab), McGill University
11:00 AM-11:45 AM	Reinforcement Learning Debate: The State of RL and The Theory-Practice Divide	(opens in new tab)	John Langford (opens in new tab), Microsoft Research Yoshua Bengio (opens in new tab), Mila (Quebec AI Institute)
11:45 AM-12:15 PM	Break
12:15 PM-1:45 PM	Virtual Poster Presentations
	Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective		Yunzong Xu, MIT
	Taylor Expansion Policy Optimization		Yunhao Tang, Columbia University
	Provably Efficient Policy Optimization with Thompson Sampling		Haque Ishfaq, McGill University
	Active Imitation Learning with Noisy Guidance		Kianté Brantley, University of Maryland
	Finite-Time Analysis of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning		Sihan Zeng, Georgia Tech
	META-Q-LEARNING		Rasool Fakoor, Amazon Web Services
	Toward the Fundamental Limits of Imitation Learning		Nived Rajaraman, UC Berkeley
	Multitask Bandit Learning through Heterogeneous Feedback Aggregation		Zhi Wang, UC San Diego
	“It’s Unwieldy and it Takes a Lot of Time.” Challenges and Opportunities for Creating Agents in Commercial Games		Mikhail Jacob, Microsoft Research, Cambridge UK
	A Framework for Robust Learning and Control of Nonlinear Systems with Large Uncertainty		Hoang Le, Microsoft Research, Redmond
	Learning Dynamic Belief Graphs to Generalize on Text-Based Games		Eric Yuan, Microsoft Research, Montreal
	Frugal Optimization for Cost-Related Hyperparameters		Qingyun Wu, Microsoft Research, NYC
	Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels		Denis Yarats, New York University
	Self Supervised Policy Adaptation During Deployment		Nicklas Hansen, Technical University of Denmark
	Multi-Task Reinforcement Learning with Soft Modularization		Ruihan Yang, UC San Diego
	Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning		Rishabh Agarwal, Google Research, and Mila Research
	A Regret Minimization Approach to Iterative Learning Control		Karan Singh, Princeton University
	RMP2: A Differentiable Policy Class for Robotic Systems with Control-Theoretic Guarantees		Anqi Li, University of Washington
	Generating Adversarial Disturbances for Controller Verification		Udaya Ghai, Princeton University

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